New research highlights LM head as a major gradient bottleneck

A new paper from arXiv, "Lost in Backpropagation: The LM Head is a Gradient Bottleneck," reveals that the final layer of language models, responsible for projecting features to vocabulary logits, acts as a significant optimization bottleneck. Researchers theoretically and empirically demonstrate that this layer compresses gradients by 95-99%, hindering the learning of even simple patterns and impacting the training dynamics of large language models. The paper argues that this inherent flaw contributes to training inefficiencies at scale, independent of model architecture, and calls for novel designs for the LM head. AI

IMPACT Identifies a fundamental training bottleneck in LLMs that may explain inefficiencies and calls for new LM head designs.

RANK_REASON Academic paper detailing a novel finding about LLM training dynamics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• Lost in Backpropagation: The LM Head is a Gradient Bottleneck
• Nathan Godey
• neural language models

• paper
• infra

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